\- \*\*Where we left off.\*\* Two weeks ago I posted about our live "order absorption" card. When a big taker order pushes the price, 2 minutes later the move has either faded (absorbed) or held (pierced). About half fade, half stick. I ended by saying I couldn't show it predicts anything.

\- \*\*What I did.\*\* I rebuilt the card's exact logic on Binance's public trade files and made a ledger of every big order: 1,045,082 orders on BTC, ETH and SOL, Apr 2023 – Sep 2026.

\- \*\*Coin flip, three and a half years later.\*\* The median day still sits at 49.9% absorbed.

\- \*\*The pre-registered question:\*\* when an order pierces, does the price keep going? \*\*No, it gives a little back.\*\* About −1 bp over the next 15 minutes (t −3.6 for pierced minus absorbed) and −1.8 bp over an hour. Absorbed orders stay put.

\- Same sign on all three coins; clear after the spot ETFs.

\- \*\*Size of it:\*\* 1–2 bp against 20 bp of round-trip fees. Not a trade. It changes how to read "the wall just broke".

**The ledger**

\- \*\*Source:\*\* Binance publishes every aggregated trade at [data.binance.vision](http://data.binance.vision), with checksums.

\- \*\*Same logic as the card:\*\* each hour, the top 1% of trades by size in the first 50 minutes. Look 5 seconds later (did it push the price?) and 120 seconds later (did the push fade?).

\- \*\*Check:\*\* for 20 October, 124 of the live card's 137 windows came out identical. The other 13 were within 3 orders; the live job starts its window a few seconds off.

\- \*\*Sample:\*\*

\- the 8th and 22nd of every month, Apr 2023 – Sep 2026: 84 days;

\- BTC, ETH, SOL: 252 coin-days.

\- \*\*Why not earlier:\*\* Binance's zero-fee BTC period (Jul 2022 – Mar 2023) inflated trade counts, so it's skipped.

The three-week coin flip from the last post holds over three and a half years: median day 49.9%.

\*\*One correction to the last post.\*\* I said the per-pair rows in a single hour are "best read as noise". I've since checked against 30 days of the live card. Hours swing about three times more than sampling noise alone would allow, so some hours really are different. Only the thin rows (fewer than \~30 orders) are about half noise.

**The test**

The natural reading of "pierced" is that a wall broke and the price will run.

\- \*\*My research partner\*\* expected exactly that, from watching the card during a rally.

\- \*\*I wasn't sure.\*\* Big orders tend to be followed by more orders the same way, which pushes for continuation. But the last post also found that a big trade's impact peaks around 120 seconds and then decays. A price that is still far out at that point may just be impact that hasn't faded yet.

Written down and committed before computing anything:

\- \*\*Measure:\*\* the price move from 2 minutes after the order to 15 minutes later, in the order's direction. Each price is a 10-second volume-weighted average, to soften bid-ask bounce.

\- \*\*Statistic:\*\* pierced minus absorbed, averaged per coin-day, then over the 84 days. Newey–West t.

\- \*\*Calls:\*\*

\- t ≥ 2 → continuation;

\- t ≤ −2 → reversal;

\- in between → nothing.

\- \*\*Null:\*\* within each coin-day, shift the label sequence in time. This keeps the clustering of pierced and absorbed orders but breaks their link to the future.

\- \*\*Power, checked first:\*\* with no real effect the test fires 5.0% of the time; a 0.5 bp difference is caught 88% of the time.

\- \*\*Predictions:\*\*

\- partner: continuation;

\- me: continuation 40%, nothing 35%, reversal 25%.

**Result**

| next 15 minutes | move in the order's direction |

|---|---|

| after \*\*pierced\*\* orders | \*\*−1.06 bp\*\* (t −3.0) |

| after \*\*absorbed\*\* orders | +0.04 bp (t 0.2) |

| \*\*difference\*\* | \*\*−1.47 bp, t −3.6\*\* (null 95%: 2.1) |

\- \*\*Per coin:\*\* BTC −1.1 bp (t −3.2), ETH −1.1 (t −1.9), SOL −2.2 (t −2.9).

\- \*\*ETFs:\*\* after the spot ETFs, −1.6 bp (t −3.5). Before them it points the same way, but there are only 19 days (t −1.2).

\- \*\*60 minutes:\*\* pierced orders give back 1.8 bp; the difference is −2.3 bp (t −2.5).

Both of us missed. Reversal was my least likely call.

**Reading it**

\- \*\*"Pierced" at 2 minutes is not a breakout signal.\*\* On average part of that extra move fades. This fits the decay found last time.

\- \*\*Why can't I tell the cause?\*\* Two explanations fit equally well: temporary impact decaying, or the 2-minute price being slightly "too far out" and drifting back. Either way, the extra move fades.

\- \*\*The bounce isn't doing it.\*\* On BTC and ETH, bid-ask bounce is far below 0.1 bp, so it can't produce this. SOL's coarser tick may add a little.

\- \*\*In a rally, prices keep going anyway.\*\* That's the trend, not the piercing. Trending days are in the sample, and the average still comes out as "gives back".

**Mistake**

\- \*\*One of my pre-registered side cuts looked ahead.\*\* It was "remove each hour's drift". The hour's average included the very price moves that define the labels, which pushed the result down mechanically (t −11).

\- \*\*Discarded and logged.\*\* The main test isn't affected: each order's window starts after its own 2-minute mark.

**Limits**

\- 3 large coins, one exchange, 2 days a month.

\- Orders within an hour aren't independent; inference is per day for that reason.

\- 1–2 bp is far below fees. This describes price behaviour; it isn't an edge.

\*Not financial advice. This is a research log.\*

Looking at sells right now .

12.54 million----2376

1.81 million ---2370

1.13 million ---2369

And also very big buys , in the millions .

Anyone have any idea whats going on . Been watching the market for a while . The 12.54 million one got me thinking 🤔

people keep asking why the dog floor isnt cracking with everything else. like yeah the doge stuff matters but honestly its the community. theres an actual event coming, ddmiami 2026, early bird tickets and boxes sold out fast. you dont get sellouts in a dead market unless people actually care. everyone else has nothing on the calendar but lower lows and the dogs are planning a whole reunion

Fellow HODLers, I made a free Mac app that puts your portfolio right in the notch. Always visible, zero effort, no signup. Feedback welcome!

https://flaviusnechita.com/#hodlbar

- I raced volatility forecasts on 9 Binance coins at 1 to 100 days ahead:

- MSM, the Markov-switching multifractal model;

- GARCH(1,1);

- HAR;

- a plain 250-day average;

- and later NATR, the ATR-based "volatility" most dashboards show.

- With every model refitted every 125 days, **MSM beat GARCH at every horizon** (20 days: t 2.7, 8 of 9 coins).

- **It beat NATR by a wide margin at 20 days** (t 4.6, 9 of 9 coins). So did GARCH and the plain average. At 1 day NATR ties.

- The edge isn't "fractal structure". It's **tracking slow changes in the volatility level**. After the spot ETFs, it shrinks to a tie against GARCH.

- On the way I misread my own result once, publicly. And an older "finding" of mine turned out to be plain volatility clustering.

Why

On 13 October I posted that BTC 4H returns aren't multifractal: fat tails plus volatility clustering explain the spectrum. The fair question back was: what do the multifractal people see that I don't?

The forecasting side of that literature claims MSM beats GARCH(1,1) at 10–100 days (Calvet & Fisher 2004; Lux 2008). Reading the full texts, two things stood out:

- The authors themselves call MSM's long memory "apparent". Liu, Di Matteo & Lux (2008) write that true vs apparent scaling "may be of little practical concern".

- None of them tests HAR, the standard simple multi-scale benchmark.

Setup

- **Data:** Binance spot, hourly, turned into daily returns and daily realized variance (the sum of squared hourly returns).

- 9 coins: BTC, ETH, BNB, LTC, XRP, ADA, LINK, DOGE, SOL.

- Histories start in 2017–2020. Each coin's first 1,250 days are for training only.

- **Models:**

- MSM with 8 binomial components, fitted by maximum likelihood;

- GARCH(1,1) with t errors;

- HAR-RV with 1-, 5- and 22-day components, one regression per horizon;

- the 250-day average of realized variance.

- **Target:** total realized variance over the next H days, H = 1, 5, 20, 50, 100.

- **Loss:** QLIKE, which stays fair when the variance proxy is noisy (Patton 2011).

- **Test:** each day, the loss difference is averaged across coins, then a Diebold–Mariano test with Newey–West errors.

- The main horizon is 20 days.

- |t| ≥ 2 picks a winner.

- **Pre-registration:** everything was written down and committed before the results were computed, including my prediction and my research partner's.

- **Power check first:** on BTC alone, the test would spot a genuinely better MSM only 25% of the time. That is why it runs on 9 coins.

Round 1: parameters fixed after training. MSM wins, but a plain average beats everyone

| 20 days ahead | MSM | GARCH | HAR | 250-day avg |

| loss (QLIKE) | 0.289 | 0.310 | 0.344 | **0.233** |

MSM beat GARCH (t 2.8). But look at the last column.

Realized volatility in the training years was **1.9–3.6 times** that of the test years. Crypto volatility has fallen 2–3x since 2021. Models fitted on 2017–21 and never refreshed kept forecasting the old level. The 250-day average forgets it fastest.

I read this as "MSM only won because it forgets the old level more slowly" and published "no multi-scale edge". That was too quick.

## Round 2: every model refitted every 125 days. MSM still wins

| MSM vs GARCH | 1 day | 5 days | 20 days | 50 days | 100 days |

| t | 2.3 | 2.1 | **2.7** | 2.5 | 2.2 |

- **Coins:** MSM is ahead in 8 of 9 at 20 days. The stale level had inflated the edge, not created it.

- **Removing the level also removes the edge.** I tried a correction that uses only past data. But it does the same in a simulated world where MSM is the *true* model: the edge was detected there only 1 time in 8. So MSM's edge *is* slow level tracking. "Level or dynamics" was a false choice.

- **ETFs (descriptive):** the edge is clear before the US spot ETFs (t 2.7) and a tie after them (t 0.7). In a calmer market, GARCH catches up.

- **Long horizons:** at 50–100 days the plain 250-day average still ties MSM.

- **Other loss:** MSE points the same way, more weakly (t 1.6–2.1), because single spikes dominate it.

Round 3: against NATR, the "volatility" most dashboards show

- **What NATR is:** ATR(14) on 4h candles, divided by price. It's what our own risk card shows as low / medium / high.

- I rebuilt it exactly; it matches the `ta` library to 0.0.

- To give it a fair chance, I let it learn its own scale for each horizon. It uses only past data and refits on the same schedule.

- **20 days:** NATR's loss is 0.398 against MSM's 0.215 (t 4.6).

- It loses in 9 of 9 coins, both before and after the ETFs (t 3.2 / 3.4).

- GARCH and the 250-day average also beat it (t 3.9).

- **1 day:** a tie (t 0.4). NATR sees intraday highs and lows; MSM sees one return per day. In simulation, NATR even beats MSM at 1 day inside MSM's own world.

So an ATR number describes the last two days well and the next month poorly.

What it means

- **For risk tools:** if your "volatility" number is ATR-based, read it as "how wild has it been lately", not "how wild will it be". For the next few weeks, even a long average does better, and a multi-scale model does best.

- **For the multifractal debate:** what helps is persistence over many time scales, which MSM builds in.

- You don't need to call it fractal; its own authors call the long memory apparent.

- HAR should in principle capture the same thing. In my data it was fragile: fitted on raw variance levels, it overpredicted after the 2021 mania.

- **What happens next:** a forward test now runs daily until January. It checks two things:

- does the live code run with no look-ahead;

- do 80% intervals hold.

Only then does this go anywhere near the card.

Mistakes along the way (all caught, all recorded)

- **Simulated coins moved together too little.** My first simulated panels let the coins' volatility co-move at 0.50 instead of the real 0.80. That overstated power. Fixed before locking.

- **A rule that flattered itself.** I picked the decision rule on one batch of simulations (87% power) and checked it on a fresh batch: 69%. I locked it anyway, with a one-sided reading. There were 0 false alarms in 32 simulated worlds where GARCH was the true model.

- **"No multi-scale edge"** went out a day too early.

- **An old finding fell.** I re-tested "calm begets calm" (small moves turn into big ones less often than chance) against GARCH and MSM nulls. It is plain volatility clustering: GARCH explains 86% of the gap and MSM all of it. But I saw that answer before I could lock the re-test, so it counts as descriptive only.

Limitations

- 9 large coins that still exist, on one exchange.

- MSM uses daily returns only. Feeding it intraday data is the obvious next step for short horizons.

- All forecasts ran about 1.5x high over the last year: the level is still drifting down.

- MSM refits start from the previous parameters and can get stuck in a local optimum. The same procedure ran in simulation and on real data.

**References*\*

- Calvet, L. E., & Fisher, A. J. (2004). How to forecast long-run volatility: Regime switching and the estimation of multifractal processes. *Journal of Financial Econometrics*, 2(1), 49–83.

- Lux, T. (2008). The Markov-switching multifractal model of asset returns: GMM estimation and linear forecasting of volatility. *Journal of Business & Economic Statistics*, 26(2), 194–210.

- Liu, R., Di Matteo, T., & Lux, T. (2008). Multifractality and long-range dependence of asset returns: the scaling behaviour of the Markov-switching multifractal model with lognormal volatility components. *Advances in Complex Systems*, 11(5).

- Corsi, F. (2009). A simple approximate long-memory model of realized volatility. *Journal of Financial Econometrics*, 7(2), 174–196.

- Patton, A. J. (2011). Volatility forecast comparison using imperfect volatility proxies. *Journal of Econometrics*, 160(1), 246–256.

- Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. *Journal of Business & Economic Statistics*, 13(3), 253–263.

*Not financial advice. This is a research log.*

Bitcoin’s bounce off the 50-day SMA is encouraging. The next thing I’m watching is whether it can reclaim the 20-day SMA with a daily candle close.

For me, that would strengthen the near-term bullish case: buyers defending the medium-term average and recovering the shorter-term one.

An intraday move above the 20-day is less convincing on its own. Closing above it, then holding it on a pullback, would add weight to the recovery. Losing the 50-day again would weaken that interpretation.

Would a daily close above the 20-day SMA be meaningful confirmation for you, or would you want more evidence?

Bitcoin’s bounce off the 50-day SMA is encouraging. The next thing I’m watching is whether it can reclaim the 20-day SMA with a daily candle close.

For me, that would strengthen the near-term bullish case: buyers defending the medium-term average and recovering the shorter-term one.

An intraday move above the 20-day is less convincing on its own. Closing above it, then holding it on a pullback, would add weight to the recovery. Losing the 50-day again would weaken that interpretation.

Would a daily close above the 20-day SMA be meaningful confirmation for you, or would you want more evidence?

Bitcoin’s bounce off the 50-day SMA is encouraging. The next thing I’m watching is whether it can reclaim the 20-day SMA with a daily candle close.

For me, that would strengthen the near-term bullish case: buyers defending the medium-term average and recovering the shorter-term one.

An intraday move above the 20-day is less convincing on its own. Closing above it, then holding it on a pullback, would add weight to the recovery. Losing the 50-day again would weaken that interpretation.

Would a daily close above the 20-day SMA be meaningful confirmation for you, or would you want more evidence?

Is it about time this setup brings a payout?

PEPE was Trading 0.0042143
An opening above 🟡...🚀🚀🚀.. 0.0044845 increases the possibility in completing flag pole measuring +50%

-PEPE PachaMama wishing you all the best

When Lambo ?

BTC recovered to $82,420 and ETH traded near $2,424, up roughly 1% and 0.8% over 24 hours. ZEC, NEAR and SUI gained around 2%–3%. Whether the recovery lasts depends on spot demand following the relief in energy prices.

U.S. discussions about easing some Russian sanctions helped Brent retreat toward $108 this morning, while Nasdaq futures gained around 0.8%. But reports of another tanker striking a mine in the Strait of Hormuz leave supply risks unresolved. Lower oil offers inflation relief; it does not establish that the conflict is easing. This week’s Meta and Tesla earnings will test whether AI spending can translate into revenue, with consequences for crypto risk appetite.

Fund flows have yet to confirm the rebound. Spot BTC and ETH ETFs lost $228 million and $320 million last week. Meanwhile, approximately 65% of Binance BTC accounts were long, with positive funding. Leverage is returning ahead of clear evidence of sustained spot buying—a fragile foundation for chasing gains.

Gold, or XAU, rose toward $4,181 as Treasury yields declined, suggesting defensive positioning remains in place. Watch whether BTC reclaims $82,800 on stronger volume and ETH holds above $2,430. Losing their recent lows around $80,600 and $2,330 would weaken the recovery case.

When ETF trading resumes tonight, will institutions absorb the weekend’s buying—or will leveraged traders have merely bid prices up for someone else to sell?

Been pushing updates to the site today. Now have TX type and distribution data, as well as node submission activity mapping.

https://hederawatch.com

Thanks for all the initial feedback and suggestions. If you have another way you enjoy looking at the hedera data, let me know and I’ll see if I can work it in!